Death by Retrospective Undersampling - Caveats and Solutions for Learning-Based MRI Reconstructions
Rajput JR, Weinmüller S, Endres J, Dawood P, Knoll F, Maier A, Zaiß M (2024)
Publication Language: English
Publication Type: Conference contribution
Publication year: 2024
Journal
Publisher: Springer
Series: Lecture Notes in Computer Science
City/Town: Cham
Book Volume: 15007
Pages Range: 233-241
Conference Proceedings Title: Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. 27th International Conference, Marrakesh, Morocco, October 6–10, 2024, Proceedings, Part VII
Event location: Marrakesh
ISBN: 9783031721038
DOI: 10.1007/978-3-031-72104-5_23
Abstract
This study challenges the validity of retrospective undersampling in MRI data science by analysis via an MRI physics simulation. We demonstrate that retrospective undersampling, a method often used to create training data for reconstruction models, can inherently alter MRI signals from their prospective counterparts. This arises from the sequential nature of MRI acquisition, where undersampling post-acquisition effectively alters the MR sequence and the magnetization dynamic in a non-linear fashion. We show that even in common sequences, this effect can make learning-based reconstructions unreliable. Our simulation provides both, (i) a tool for generating accurate prospective undersampled datasets for analysis of such effects, or for MRI training data augmentation, and (ii) a differentiable reconstruction operator that models undersampling correctly. The provided insights are crucial for the development and evaluation of AI-driven acceleration of diagnostic MRI tools.
Authors with CRIS profile
Junaid Rasool Rajput
Lehrstuhl für Informatik 5 (Mustererkennung)
Simon Weinmüller
Neuroradiologisches Institut
Jonathan Endres
Neuroradiologisches Institut
Florian Knoll
Professur für Computational Imaging
Andreas Maier
Lehrstuhl für Informatik 5 (Mustererkennung)
Moritz Zaiß
Professur für Multimodale Bildgebung in der klinischen Forschung
How to cite
APA:
Rajput, J.R., Weinmüller, S., Endres, J., Dawood, P., Knoll, F., Maier, A., & Zaiß, M. (2024). Death by Retrospective Undersampling - Caveats and Solutions for Learning-Based MRI Reconstructions. In Marius George Linguraru, Qi Dou, Aasa Feragen, Stamatia Giannarou, Ben Glocker, Karim Lekadir, Julia A. Schnabel (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. 27th International Conference, Marrakesh, Morocco, October 6–10, 2024, Proceedings, Part VII (pp. 233-241). Marrakesh, MA: Cham: Springer.
MLA:
Rajput, Junaid Rasool, et al. "Death by Retrospective Undersampling - Caveats and Solutions for Learning-Based MRI Reconstructions." Proceedings of the 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024, Marrakesh Ed. Marius George Linguraru, Qi Dou, Aasa Feragen, Stamatia Giannarou, Ben Glocker, Karim Lekadir, Julia A. Schnabel, Cham: Springer, 2024. 233-241.
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